{"id":"https://openalex.org/W3008849576","doi":"https://doi.org/10.1109/tits.2020.2973279","title":"Daily Traffic Flow Forecasting Through a Contextual Convolutional Recurrent Neural Network Modeling Inter- and Intra-Day Traffic Patterns","display_name":"Daily Traffic Flow Forecasting Through a Contextual Convolutional Recurrent Neural Network Modeling Inter- and Intra-Day Traffic Patterns","publication_year":2020,"publication_date":"2020-02-24","ids":{"openalex":"https://openalex.org/W3008849576","doi":"https://doi.org/10.1109/tits.2020.2973279","mag":"3008849576"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2020.2973279","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2020.2973279","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024477428","display_name":"Dongfang Ma","orcid":"https://orcid.org/0000-0002-9334-1570"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]},{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongfang Ma","raw_affiliation_strings":["Institute of Marine Information Science and Technology, Zhejiang University, Hangzhou, China","Research Center for Artificial Intelligence, Peng Cheng Laboratory, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-9334-1570","affiliations":[{"raw_affiliation_string":"Institute of Marine Information Science and Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"Research Center for Artificial Intelligence, Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078155030","display_name":"Xiang Song","orcid":"https://orcid.org/0000-0001-5429-6749"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiang Song","raw_affiliation_strings":["Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA"],"raw_orcid":"https://orcid.org/0000-0001-5429-6749","affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072019525","display_name":"Pu Li","orcid":"https://orcid.org/0000-0003-4874-5573"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pu Li","raw_affiliation_strings":["College of Management and Economics, Dalian University of Technology, Dalian, China"],"raw_orcid":"https://orcid.org/0000-0003-4874-5573","affiliations":[{"raw_affiliation_string":"College of Management and Economics, Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":12.5153,"has_fulltext":false,"cited_by_count":181,"citation_normalized_percentile":{"value":0.9942036,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"22","issue":"5","first_page":"2627","last_page":"2636"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.9944000244140625,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10698","display_name":"Transportation Planning and Optimization","score":0.9918000102043152,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.781021237373352},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.774711549282074},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7734615802764893},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7319406270980835},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7042098045349121},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.6767488121986389},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6045079231262207},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5928459167480469},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.5567262768745422},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5037617087364197},{"id":"https://openalex.org/keywords/traffic-generation-model","display_name":"Traffic generation model","score":0.46692702174186707},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4588012099266052},{"id":"https://openalex.org/keywords/advanced-traffic-management-system","display_name":"Advanced Traffic Management System","score":0.4216899871826172},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39627692103385925},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.22618785500526428},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1635911464691162},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.12532690167427063}],"concepts":[{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.781021237373352},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.774711549282074},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7734615802764893},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7319406270980835},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7042098045349121},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.6767488121986389},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6045079231262207},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5928459167480469},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.5567262768745422},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5037617087364197},{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.46692702174186707},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4588012099266052},{"id":"https://openalex.org/C42693407","wikidata":"https://www.wikidata.org/wiki/Q4686317","display_name":"Advanced Traffic Management System","level":3,"score":0.4216899871826172},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39627692103385925},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.22618785500526428},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1635911464691162},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.12532690167427063},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2020.2973279","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2020.2973279","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1856576220","display_name":"\u4eba\u5de5\u667a\u80fd\u9a71\u52a8\u7684\u57ce\u5e02\u4ea4\u901a\u63a7\u5236\u7406\u8bba\u4e0e\u65b9\u6cd5","funder_award_id":"LR19F030002","funder_id":"https://openalex.org/F4320338464","funder_display_name":"Natural Science Foundation of Zhejiang Province"},{"id":"https://openalex.org/G5568186771","display_name":null,"funder_award_id":"2018QNA4050","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G7370932115","display_name":null,"funder_award_id":"61773338","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8237307337","display_name":null,"funder_award_id":"61773337","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null},{"id":"https://openalex.org/F4320338464","display_name":"Natural Science Foundation of Zhejiang Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W32235646","https://openalex.org/W43001522","https://openalex.org/W1875626450","https://openalex.org/W1991250587","https://openalex.org/W1994335611","https://openalex.org/W1998210776","https://openalex.org/W2004353783","https://openalex.org/W2011504567","https://openalex.org/W2016976804","https://openalex.org/W2036785686","https://openalex.org/W2040297119","https://openalex.org/W2047493229","https://openalex.org/W2051241238","https://openalex.org/W2058532765","https://openalex.org/W2077466428","https://openalex.org/W2087952713","https://openalex.org/W2103400647","https://openalex.org/W2108196201","https://openalex.org/W2137214660","https://openalex.org/W2139606794","https://openalex.org/W2146688411","https://openalex.org/W2150010190","https://openalex.org/W2156793027","https://openalex.org/W2165137963","https://openalex.org/W2344029946","https://openalex.org/W2460404912","https://openalex.org/W2573587735","https://openalex.org/W2575125657","https://openalex.org/W2593182953","https://openalex.org/W2594893535","https://openalex.org/W2792326773","https://openalex.org/W2793820729","https://openalex.org/W2808990700","https://openalex.org/W2890116178","https://openalex.org/W2896827527","https://openalex.org/W4242121569","https://openalex.org/W6659849045"],"related_works":["https://openalex.org/W3158831960","https://openalex.org/W2521728836","https://openalex.org/W2972088616","https://openalex.org/W3094593199","https://openalex.org/W2008793610","https://openalex.org/W2077294583","https://openalex.org/W2893934742","https://openalex.org/W4310082270","https://openalex.org/W4315796051","https://openalex.org/W2372059048"],"abstract_inverted_index":{"Traffic":[0],"flow":[1,40,94,102,123],"forecasting":[2,41,95,182],"is":[3,114,151,184],"an":[4],"important":[5],"problem":[6],"for":[7,19,38,91],"the":[8,35,70,76,79,138,155,166],"successful":[9],"deployment":[10],"of":[11,142,148],"intelligent":[12],"transportation":[13],"systems,":[14],"which":[15,174],"has":[16],"been":[17],"studied":[18],"more":[20],"than":[21],"two":[22],"decades.":[23],"In":[24,82],"recent":[25],"years,":[26],"deep":[27,55],"learning":[28,56],"methods":[29,57,179],"are":[30,51,128],"emerging":[31],"to":[32,43,116,136,153],"serve":[33],"as":[34,67,69],"benchmark":[36,178],"tool":[37],"traffic":[39,65,80,93,101,122,143],"due":[42],"its":[44,181],"superior":[45],"prediction":[46,156,172],"performance.":[47,157],"However,":[48],"most":[49],"studies":[50],"based":[52],"on":[53],"simple":[54],"that":[58,165],"can":[59,104],"not":[60],"capture":[61],"inter-":[62,118],"and":[63,78,100,119,180],"intra-day":[64,139],"patterns":[66,103],"well":[68],"correlation":[71],"between":[72],"contextual":[73,98,146],"factors":[74,99],"like":[75],"weather":[77],"flow.":[81,144],"this":[83],"paper,":[84],"we":[85,163],"propose":[86],"a":[87,108,159],"novel":[88],"deep-learning-based":[89],"method":[90],"daily":[92],"where":[96],"incorporating":[97],"be":[105],"critical.":[106],"First,":[107],"particular":[109],"convolutional":[110],"neural":[111],"network":[112],"(CNN)":[113],"deployed":[115],"extract":[117],"intra-":[120],"day":[121],"patterns.":[124],"Then":[125],"extracted":[126],"features":[127],"fed":[129],"into":[130],"long":[131],"short-term":[132],"memory":[133],"(LSTM)":[134],"units":[135],"learn":[137],"temporal":[140],"evolution":[141],"Finally,":[145],"information":[147],"historical":[149],"days":[150],"integrated":[152],"enhance":[154],"Through":[158],"real-data":[160],"case":[161],"study,":[162],"show":[164],"proposed":[167],"approach":[168],"achieves":[169],"over":[170],"90%":[171],"accuracy":[173],"greatly":[175],"outperforms":[176],"existing":[177],"performance":[183],"robust":[185],"under":[186],"various":[187],"scenarios.":[188]},"counts_by_year":[{"year":2026,"cited_by_count":14},{"year":2025,"cited_by_count":22},{"year":2024,"cited_by_count":33},{"year":2023,"cited_by_count":39},{"year":2022,"cited_by_count":38},{"year":2021,"cited_by_count":26},{"year":2020,"cited_by_count":9}],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2025-10-10T00:00:00"}